An "R" package for automatic download and preprocessing of MODIS Land Products Time Series
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Updated
Jul 28, 2024 - R
An "R" package for automatic download and preprocessing of MODIS Land Products Time Series
Using GEE to collect and discover land surface temperature data over European river basins.
MODIS Assimilation and Processing Engine
Using GEE to collect and discover land surface temperature data over custom location input.
MODIS Mosaic of Antarctica
Research work for cloud and snow segmentation problem using meteorological satellite Electro-L №2 multispectral data, also suitable for GOES-16,17 multispectral data. This project includes all needed functions and utils for preprocessing multispectral data to make your own dataset for cloud and (or) snow segmentation problem
Deep learning for Synthetic Aperture Radar(SAR) and Radiometry data. An Ensemble Convolutional Neural Network workflow is implemented with data acquisition, processing, labelling, creating model, training model and launching a model
Canada Wildfire Prediction Using Deep Learning
GEE code for pixel-based land cover classification with Random Forest (RF) algorithm, and for NDVI time series visualization.
Distributed Remote Sensing Processing
Niger Economic Monitoring
Análisis y construcción de modelo de calidad del aire - Sitio: Comunidad de Valencia (España)
MODIS NDVI data is processed to create a time series visualization for the Nyando catchment area within the Lake Victoria Basin.
Spatiotemporal Analysis of Agricultural Drought Severity and Hotspots in Somaliland. It integrates MODIS-derived vegetation indices and CHIRPS precipitation data to identify and assess drought severity and hotspots over time.
Jupyter notebooks created for use in a training session on the topic of drought forecasting via satellite:artificial_satellite:. This repo contains the scripts needed to pre-process MODIS data and apply Gaussian Processes to time-series in order to forecast VCI :chart_with_upwards_trend:.
NASA Space Apps Challenge 2020 submission. Team: Garlic Bread
Phoenix is a realtime forest-fire prediction app | NASA Space Apps Challenge 2020 | Global Nominee
Python package to crawl and analyze historical satellite images (MODIS) of volcano hotspots and filter clouds via spectral imaging
UPC Artificial Intelligence with Deep Learning. Wildfire prediction using Semantic Segmentation on satellite imagery
This is the code used in my masters thesis research.
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